About the Course

The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners....

Top reviews

NT

Mar 05, 2018

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Capstone did provide a true test of Data Analytics skills. Its like a being left alone in a jungle to survive for a month. Either you succumb to nature or come out alive with a smile and confidence.

SS

Mar 29, 2017

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Wow i finally managed to finish the specialization!! definitely learned a lot and also found out difficulties in building predictors by trying to balancing speed, accuracy and memory constraints!!!

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201 - 225 of 253 Reviews for Data Science Capstone

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By Humberto R

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Apr 09, 2018

Very instructive, since it presents you with a real world problem, that you need to solve by yourself, in all of its complexity.

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By Jeremi S

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Dec 07, 2018

Challenging. The course could possibly offer a 'here's how it could be done' ideal example after final submission and pass.

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By xuanru s

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Jun 20, 2017

Very challenge work. new topic. The only issue is if there is any videos that could guide us would be better.

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By Zaman F

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Aug 24, 2017

Most of the courses were very well tought and contained useful material.

Thanks to all three instructors

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By Kalyan S M

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Nov 06, 2016

Really great course to apply all the techniques learned earlier in the specialization.

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By Marcus S

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Sep 20, 2016

A good & fun idea to implement. Would have prefered implementing my own idea though.

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By Rudolf E

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Jun 20, 2017

Great course, great content, didn't like the final capstone project though.

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By Emi H

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Jun 22, 2017

Good project. Got me to think outside the box and really challenge myself.

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By HIN-WENG W

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Aug 27, 2017

Challenging real life project that apply the academic knowledge

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By Greig R

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Mar 16, 2018

A tricky end to the specialisation - but quite a lot of fun.

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By Chonlatit P

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Jun 27, 2019

Project is good for practice what you've learnt

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By Murray S

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Oct 09, 2016

Good test of what we learned in the courses.

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By Ajay K P

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Mar 30, 2018

I really had fun working on this project.

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By Artem V

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Sep 14, 2017

Nice balance of focused and open-ended

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By Gary B

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Sep 15, 2017

tough capstone and took a lot of time

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By Yew C C

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Jul 20, 2016

Good and interesting project.

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By Tiberiu D O

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Sep 22, 2017

Interesting assignment!

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By Sabawoon S

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Nov 25, 2017

Excellent course.

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By Filipe R

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Oct 07, 2018

Great project.

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By Kevin M

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Jan 15, 2018

Very hard!

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By Richard I C

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Jul 19, 2016

As a capstone to a series of courses that covered data science and R, I found this one to be a bit lacking. There was no involvement from the professors at JHU or the folks at SwiftKey. As was mentioned in another review, the course feels abandoned. All you get a few short (two minutes or so) videos that give you little in the way of instruction or direction. Basically, they just say, "Go do this. Good luck!"

There were also no Mentors or TAs to guide students or answer questions. It was the students helping each other through the forums. Sometimes it was helpful and everyone involved learned something. Other times, it was the blind trying to lead the visually-impaired.

On a positive note, you will use all of the skills from the previous courses: writing R functions, performing exploratory analysis and publishing it via RPubs. Your final product will be displayed for everyone via ShinyApps and a presentation using R Presentation (also published via RPubs).

On a(nother) negative note, the topic of Natural Language Processing is not an easy one to just walk into and feel confident in providing a working next-word prediction algorithm in about eight (8) weeks. You're reading academic journal articles, watching multiple videos from another Coursera course (which actually focuses on the topic of NLP, and takes place over several courses and several months!).

Supposedly, there is work going on to update the course, so hopefully future students will get a better experience. I did take a bit away from this course, especially since I made more than one attempt to complete it. However, it was definitely a shock to find myself missing those things that one typically finds in a learning environment -- descriptive background, assistance to problems, etc. -- and seeing that I was for all intents and purposes on my own. Even in the professional world of data analysis, I have never experienced the lack of support that I found in this course.

With that, I am giving it three (3) stars. As I said, I did learn a bit, but it was a bit of a struggle that required multiple attempts to complete. This would have been better off as a stand alone topic (which it already is by another Coursera affiliated school), or having a capstone course that builds on a topic more in the wheelhouse of the JHU professors: a capstone project focusing on bioinformatics or biostatistics would have been amazing in comparison to this.

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By David M

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Jul 21, 2016

This was essentially a self-study project with some social peers. The topic, approach, and standards were different from all of the other units in the Data Science specialization. I found the other units more enjoyable.

Learning the essentials of NLP quickly is necessary to begin the project. I ordered a textbook, for example, and I was fortunate that it arrived quickly. If NLP is a prerequisite for this capstone project - whether in the form of a prior class or textbook knowledge - this should be indicated clearly on the course description page.

Nevertheless, the main learning that I achieved with this course was in the area of software engineering - specifically, how to take advantage of vectorization in R to achieve reasonable computing performance. While this is a valuable skill, it doesn't seem the proper focus of a capstone course in a sequence focused primarily on other topics.

As noted elsewhere in these comments, there was a complete absence of any traditional teaching support. Learning outcomes suffered as result. The missing resources included instructors, mentors, partners, and learning materials.

The course site notes an expected time requirement of a few hours per week. My commitment was 20 hours per week, under some pressure. Numerous students take this "course" multiple time, in order to arrange for reasonable software development time.

Producing working software was fun, as it always is. The course learner community was supportive, which is fortunately typical for Coursera.

All in all, this project was *not* an effective capstone for the Data Science specialization. The project was interesting in its way, but it felt 'parachuted in' to this learning sequence.

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By Guilherme B D J

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Mar 24, 2017

The main reason for my rating is because the course is so "loose" on what your are supposed to achieve incrementally every week that it can lead to some hard situations.

Just to give my example: the first week was piece of cake and I didn't feel like it really contribute for the following weeks. Then, I was struggling with the suggested library (tm) until I got support through the discussion forums and someone suggested me to use quanteda.

Then thinks started to run smoothly, or so I thought. When implementing the language model (which, at first, I thought was supposed to be KBO), I got stuck for a long period. Not because my model was wrong (I was able to implement it and to check it against some hand-written and proved examples - which I should probably thank again), but because I was not able to make it run efficiently enough for the given constraints.

Being stuck in this stage for longer than I wanted, I had to sacrifice another important steps of data analysis pipeline in order to not jeopardize my final delivery by not meeting the final due date. I know that this is exactly what will happen in the "real" life, but I think that some better guidance could guarantee the students spent a more even amount of time in across all steps.

All things considered, I think the Capstone was really interesting and likely took more than the 4-9 hours per week, but most of this is probably because of the problems I faced.

I believe that with a better guidance on the paths to follow or maybe some suggested libraries to use, a lot of "noise" (useless difficulty) could be removed and this course would definitely get more starts.

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By Diego C G

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Apr 13, 2016

Could be better. The teacher sometimes explain the concepts in a hard way, and not always shows how to do in practice.

But you will get curious and in case of doubts, you can find more simple explanations on the web, and the forum is very good.

The assignments are hard, you will need do research to accomplish then, but is the best way to learn.

I think the specialization is good to someone without much knowledge on the field (like me). But it's only the start!

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By Andrew S

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Jun 26, 2017

I felt this course was the weakest of the series. The capstone focuses on building an NLP application, which although I find interesting, does not make for a good final problem as NLP was not really covered in the specialization and NLP is particularly challenging in R. That said, the series as a whole is well worth the time and effort.